Predicting Infectiousness for Proactive Contact Tracing
- URL: http://arxiv.org/abs/2010.12536v1
- Date: Fri, 23 Oct 2020 17:06:07 GMT
- Title: Predicting Infectiousness for Proactive Contact Tracing
- Authors: Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin
Weiss, Tristan Deleu, Eilif Muller, Meng Qu, Victor Schmidt, Pierre-Luc
St-Charles, Hannah Alsdurf, Olexa Bilanuik, David Buckeridge, G\'aetan
Marceau Caron, Pierre-Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris
Pal, Irina Rish, Bernhard Sch\"olkopf, Abhinav Sharma, Jian Tang, Andrew
Williams
- Abstract summary: Large-scale digital contact tracing is a potential solution to resume economic and social activity while minimizing spread of the virus.
Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health.
This paper develops and test methods that can be deployed to a smartphone to proactively predict an individual's infectiousness.
- Score: 75.62186539860787
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual
contact tracing in many countries and resulting in widespread lockdowns for
emergency containment. Large-scale digital contact tracing (DCT) has emerged as
a potential solution to resume economic and social activity while minimizing
spread of the virus. Various DCT methods have been proposed, each making
trade-offs between privacy, mobility restrictions, and public health. The most
common approach, binary contact tracing (BCT), models infection as a binary
event, informed only by an individual's test results, with corresponding binary
recommendations that either all or none of the individual's contacts
quarantine. BCT ignores the inherent uncertainty in contacts and the infection
process, which could be used to tailor messaging to high-risk individuals, and
prompt proactive testing or earlier warnings. It also does not make use of
observations such as symptoms or pre-existing medical conditions, which could
be used to make more accurate infectiousness predictions. In this paper, we use
a recently-proposed COVID-19 epidemiological simulator to develop and test
methods that can be deployed to a smartphone to locally and proactively predict
an individual's infectiousness (risk of infecting others) based on their
contact history and other information, while respecting strong privacy
constraints. Predictions are used to provide personalized recommendations to
the individual via an app, as well as to send anonymized messages to the
individual's contacts, who use this information to better predict their own
infectiousness, an approach we call proactive contact tracing (PCT). We find a
deep-learning based PCT method which improves over BCT for equivalent average
mobility, suggesting PCT could help in safe re-opening and second-wave
prevention.
Related papers
- Protect Your Score: Contact Tracing With Differential Privacy Guarantees [68.53998103087508]
We argue that privacy concerns currently hold deployment back.
We propose a contact tracing algorithm with differential privacy guarantees against this attack.
Especially for realistic test scenarios, we achieve a two to ten-fold reduction in the infection rate of the virus.
arXiv Detail & Related papers (2023-12-18T11:16:33Z) - COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital
Contact Tracing [68.68882022019272]
COVI-AgentSim is an agent-based compartmental simulator based on virology, disease progression, social contact networks, and mobility patterns.
We use COVI-AgentSim to perform cost-adjusted analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features.
arXiv Detail & Related papers (2020-10-30T00:47:01Z) - Epidemic mitigation by statistical inference from contact tracing data [61.04165571425021]
We develop Bayesian inference methods to estimate the risk that an individual is infected.
We propose to use probabilistic risk estimation in order to optimize testing and quarantining strategies for the control of an epidemic.
Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact.
arXiv Detail & Related papers (2020-09-20T12:24:45Z) - PrivyTRAC: Privacy and Security Preserving Contact Tracing System [0.0]
Smartphone location-based methods have been proposed and implemented as an effective alternative to traditional labor intensive contact tracing methods.
There are serious privacy and security concerns that may impede wide-spread adoption in many societies.
A new system concept, called PrivyTRAC, preserves user privacy, increases security and improves accuracy of smartphone contact tracing.
arXiv Detail & Related papers (2020-06-15T17:32:38Z) - COVI White Paper [67.04578448931741]
Contact tracing is an essential tool to change the course of the Covid-19 pandemic.
We present an overview of the rationale, design, ethical considerations and privacy strategy of COVI,' a Covid-19 public peer-to-peer contact tracing and risk awareness mobile application developed in Canada.
arXiv Detail & Related papers (2020-05-18T07:40:49Z) - Digital Ariadne: Citizen Empowerment for Epidemic Control [55.41644538483948]
The COVID-19 crisis represents the most dangerous threat to public health since the H1N1 pandemic of 1918.
Technology-assisted location and contact tracing, if broadly adopted, may help limit the spread of infectious diseases.
We present a tool, called 'diAry' or 'digital Ariadne', based on voluntary location and Bluetooth tracking on personal devices.
arXiv Detail & Related papers (2020-04-16T15:53:42Z) - Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic [10.42119408384899]
Smart phones can be used to quickly identify infected individuals during an epidemic.
First-generation contact tracing tools have been used to expand mass surveillance, limit individual freedoms and expose the most private details about individuals.
We describe advanced security enhancing approaches that can mitigate these risks and describe trade-offs one must make when developing and deploying any mass contact-tracing technology.
arXiv Detail & Related papers (2020-03-19T04:22:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.